Comparison of Kernel Support Vector Machine (SVM) in Classification of Human Development Index (HDI)

Abstract

Human Development Index (HDI) is one of measuring instrument of achieving quality of life of one region even country. There are three basic components of the Human Development Index compilers: health dimension, knowledge dimension, and decent living dimension. Classification is a method for compiling data systematically according to the rules that have been set previously. In recent years, classification method has been proven to help many people’s work, such as image classification, medical biology, traffic light, text classification etc. There are many methods to solve classification problem. This variation method makes the researchers find it difficult to determine which method is best for a problem this framework is aimed to compare the ability of classification methods, such as Support Vector Machine (SVM) Linear Kernel, Radial Basis Function (RBF) Kernel and Polynomial kernel methods. The result of classification of HDI by using RBF kernel is the best kernel to solve HDI problem, with parameter combination cost= 1 and gamma=1 obtained classification accuracy of 98.1% which is the best classification accuracy.